https://github.com/thunlp/DocRED

Dataset and codes for ACL 2019 DocRED: A Large-Scale Document-Level Relation Extraction Dataset.
https://github.com/thunlp/DocRED

Last synced: about 1 month ago
JSON representation

Dataset and codes for ACL 2019 DocRED: A Large-Scale Document-Level Relation Extraction Dataset.

• Host: GitHub
• URL: https://github.com/thunlp/DocRED
• Owner: thunlp
• License: mit
• Created: 2019-06-03T10:43:17.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-12-01T03:18:37.000Z (about 4 years ago)
• Last Synced: 2024-11-10T18:45:47.805Z (about 1 month ago)
• Language: Python
• Homepage:
• Size: 55.7 KB
• Stars: 617
• Watchers: 18
• Forks: 113
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• StarryDivineSky - thunlp/DocRED

README

        
# DocRED

Dataset and code for baselines for [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://arxiv.org/abs/1906.06127v3)

Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: 

+ DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.

+ DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.

+ Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.

## Codalab

If you are interested in our dataset, you are welcome to join in the Codalab competition at [DocRED](https://competitions.codalab.org/competitions/20717)

## Cite

If you use the dataset or the code, please cite this paper:

```

@inproceedings{yao2019DocRED,

  title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},

  author={Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong},

  booktitle={Proceedings of ACL 2019},

  year={2019}

}

```